Join our global team of experts
At Diconium, you will work on projects that connect software, data, AI, and human expertise to create digital products, services, and operating models that deliver measurable business impact. Together with 2,500 experts across our international teams, you will help build, run, and scale intelligence across the full value chain.
In everything we do, we prioritize people and genuine human connections, ensuring a supportive and inclusive work environment. Our hybrid workplace also provides a high degree of flexibility.
WHAT YOU CAN EXPECT
You will design, build, and maintain scalable batch and real-time data pipelines, processing structured, semi-structured, and unstructured data using AWS-native services and distributed data processing frameworks.
You will work on innovative and fast-paced digital transformation projects for global customers, delivering data-driven solutions that create measurable business value.
You will architect and implement cloud-native data platforms on AWS, enabling efficient data ingestion, transformation, storage, and analytics at scale.
You will leverage AWS services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon EMR, AWS Lambda, Amazon Kinesis, and DynamoDB to build robust and cost-efficient data solutions.
You will collaborate closely with Data Scientists, Data Analysts, Product Teams, and Software Engineers to identify data requirements and translate them into scalable technical solutions.
You will evaluate and introduce new technologies, frameworks, and best practices in data engineering to continuously improve platform performance, reliability, and maintainability.
You will contribute to technical leadership within the team, mentoring colleagues, sharing expertise, and supporting the growth of our data engineering capabilities.
You will develop and maintain documentation, architecture diagrams, and operational best practices for data platforms and pipelines.
WHAT YOU BRING ALONG
Must Have
8+ years of hands-on experience in Data Engineering, building and operating large-scale data platforms and pipelines in cloud environments.
Strong programming skills in Python and SQL, with extensive experience using Spark, PySpark, Spark SQL, or other distributed processing frameworks.
Proven experience designing and implementing end-to-end data solutions on AWS.
Strong hands-on experience with: Amazon S3 AWS Glue Amazon Redshift AWS Lambda Amazon EMR Amazon RDS Amazon DynamoDB AWS IAM Amazon VPC Amazon CloudWatch
Experience building modern ETL/ELT frameworks and data integration solutions across multiple source systems.
Solid understanding of data modeling, data warehousing, dimensional modeling, and data architecture principles.
Experience working with orchestration and workflow tools such as AWS Step Functions, Apache Airflow, or AWS Managed Workflows for Apache Airflow (MWAA).
Strong experience with Unix/Linux environments and Bash scripting for automation and operational support.
Experience implementing security, governance, and access control best practices in AWS environments.
Excellent communication skills and experience working in cross-functional Agile teams.
Nice to Have
Experience with real-time and event-driven architectures using Amazon Kinesis, Apache Kafka, Amazon MSK, or similar streaming technologies.
Hands-on experience with Data Lake and Lakehouse architectures, including AWS Lake Formation, Apache Iceberg, Delta Lake, or Apache Hudi.
Experience with Infrastructure as Code (IaC) using Terraform, AWS CloudFormation, or AWS CDK.
Experience implementing CI/CD pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or AWS CodePipeline.
Knowledge of containerization and orchestration technologies such as Docker, Amazon ECS, Amazon EKS, and Kubernetes.
Familiarity with monitoring and observability tools such as CloudWatch, Grafana, Prometheus, and Datadog.
AWS certifications such as: AWS Certified Data Engineer – Associate AWS Certified Solutions Architect –…